"""simple docstring"""


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


a				=logging.get_logger(__name__)  # pylint: disable=invalid-name

a				='\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=8							)				->   Tuple:




						'''simple docstring'''



						lowerCamelCase__   =height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowerCamelCase__   =width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					):
										super().__init__()

										self.register_modules(
										    unet=_lowerCamelCase      ,					scheduler=_lowerCamelCase      ,					movq=_lowerCamelCase      ,					)
										lowerCamelCase__   =2 ** (len(self.movq.config.block_out_channels ) - 1)

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										if latents is None:
																lowerCamelCase__   =randn_tensor(_lowerCamelCase      ,					generator=_lowerCamelCase      ,					device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										else:
																if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
																lowerCamelCase__   =latents.to(_lowerCamelCase )

										lowerCamelCase__   =latents * scheduler.init_noise_sigma
										return latents

				def 						_a      ( self      ,					_lowerCamelCase=0 ):
										if is_accelerate_available():
																from accelerate import cpu_offload
										else:
																raise ImportError("Please install accelerate via `pip install accelerate`" )

										lowerCamelCase__   =torch.device(F'''cuda:{gpu_id}''' )

										lowerCamelCase__   =[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(_lowerCamelCase      ,					_lowerCamelCase )

				def 						_a      ( self      ,					_lowerCamelCase=0 ):
										if is_accelerate_available() and is_accelerate_version(">="      ,					"0.17.0.dev0" ):
																from accelerate import cpu_offload_with_hook
										else:
																raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )

										lowerCamelCase__   =torch.device(F'''cuda:{gpu_id}''' )

										if self.device.type != "cpu":
																self.to("cpu"      ,					silence_dtype_warnings=_lowerCamelCase )
																torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

										lowerCamelCase__   =None
										for cpu_offloaded_model in [self.unet, self.movq]:
																lowerCamelCase__   , lowerCamelCase__   =cpu_offload_with_hook(_lowerCamelCase      ,					_lowerCamelCase      ,					prev_module_hook=_lowerCamelCase )

										# We'll offload the last model manually.
										lowerCamelCase__   =hook

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 						_a      ( self ):
										if not hasattr(self.unet      ,					"_hf_hook" ):
																return self.device
										for module in self.unet.modules():
																if (
																    hasattr(_lowerCamelCase      ,					"_hf_hook" )
																    and hasattr(module._hf_hook      ,					"execution_device" )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device )
										return self.device






				@torch.no_grad()
				@replace_example_docstring(_lowerCamelCase )
				def __call__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = 512      ,					_lowerCamelCase = 512      ,					_lowerCamelCase = 100      ,					_lowerCamelCase = 4.0      ,					_lowerCamelCase = 1      ,					_lowerCamelCase = None      ,					_lowerCamelCase = None      ,					_lowerCamelCase = "pil"      ,					_lowerCamelCase = True      ,					):
										lowerCamelCase__   =self._execution_device

										lowerCamelCase__   =guidance_scale > 1.0

										if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																lowerCamelCase__   =torch.cat(_lowerCamelCase      ,					dim=0 )
										lowerCamelCase__   =image_embeds.shape[0] * num_images_per_prompt
										if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																lowerCamelCase__   =torch.cat(_lowerCamelCase      ,					dim=0 )

										if do_classifier_free_guidance:
																lowerCamelCase__   =image_embeds.repeat_interleave(_lowerCamelCase      ,					dim=0 )
																lowerCamelCase__   =negative_image_embeds.repeat_interleave(_lowerCamelCase      ,					dim=0 )

										lowerCamelCase__   =torch.cat([negative_image_embeds, image_embeds]      ,					dim=0 ).to(dtype=self.unet.dtype      ,					device=_lowerCamelCase )

										self.scheduler.set_timesteps(_lowerCamelCase      ,					device=_lowerCamelCase )
										lowerCamelCase__   =self.scheduler.timesteps

										lowerCamelCase__   =self.unet.config.in_channels

										lowerCamelCase__   , lowerCamelCase__   =downscale_height_and_width(_lowerCamelCase      ,					_lowerCamelCase      ,					self.movq_scale_factor )

										# create initial latent
										lowerCamelCase__   =self.prepare_latents(
										    (batch_size, num_channels_latents, height, width)      ,					image_embeds.dtype      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					self.scheduler      ,					)

										for i, t in enumerate(self.progress_bar(_lowerCamelCase ) ):
																# expand the latents if we are doing classifier free guidance
																lowerCamelCase__   =torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

																lowerCamelCase__   ={"image_embeds": image_embeds}
																lowerCamelCase__   =self.unet(
																    sample=_lowerCamelCase      ,					timestep=_lowerCamelCase      ,					encoder_hidden_states=_lowerCamelCase      ,					added_cond_kwargs=_lowerCamelCase      ,					return_dict=_lowerCamelCase      ,					)[0]

																if do_classifier_free_guidance:
																						lowerCamelCase__   , lowerCamelCase__   =noise_pred.split(latents.shape[1]      ,					dim=1 )
																						lowerCamelCase__   , lowerCamelCase__   =noise_pred.chunk(2 )
																						lowerCamelCase__   , lowerCamelCase__   =variance_pred.chunk(2 )
																						lowerCamelCase__   =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																						lowerCamelCase__   =torch.cat([noise_pred, variance_pred_text]      ,					dim=1 )

																if not (
																    hasattr(self.scheduler.config      ,					"variance_type" )
																    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																):
																						lowerCamelCase__   , lowerCamelCase__   =noise_pred.split(latents.shape[1]      ,					dim=1 )

																# compute the previous noisy sample x_t -> x_t-1
																lowerCamelCase__   =self.scheduler.step(
																    _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					generator=_lowerCamelCase      ,					)[0]
										# post-processing
										lowerCamelCase__   =self.movq.decode(_lowerCamelCase      ,					force_not_quantize=_lowerCamelCase )["sample"]

										if output_type not in ["pt", "np", "pil"]:
																raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )

										if output_type in ["np", "pil"]:
																lowerCamelCase__   =image * 0.5 + 0.5
																lowerCamelCase__   =image.clamp(0      ,					1 )
																lowerCamelCase__   =image.cpu().permute(0      ,					2      ,					3      ,					1 ).float().numpy()

										if output_type == "pil":
																lowerCamelCase__   =self.numpy_to_pil(_lowerCamelCase )

										if not return_dict:
																return (image,)

										return ImagePipelineOutput(images=_lowerCamelCase )



"""simple docstring"""


from __future__ import annotations

import math





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if len(__lowerCAmelCase							) != 2 or len(a[0]							) != 2 or len(__lowerCAmelCase							) != 2 or len(b[0]							) != 2:
												raise Exception("Matrices are not 2x2"							)
						lowerCamelCase__   =[
						    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
						    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
						]
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Dict:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[list, list, list, list]:




						'''simple docstring'''



						if len(__lowerCAmelCase							) % 2 != 0 or len(a[0]							) % 2 != 0:
												raise Exception("Odd matrices are not supported!"							)

						lowerCamelCase__   =len(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_length // 2

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[
						    [a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)
						]

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)]

						return top_left, top_right, bot_left, bot_right





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[int, int]:




						'''simple docstring'''



						return len(__lowerCAmelCase							), len(matrix[0]							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						print("\n".join(str(__lowerCAmelCase							) for line in matrix							)							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							) == (2, 2):
												return default_matrix_multiplication(__lowerCAmelCase  ,       __lowerCAmelCase							)

						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)
						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)

						lowerCamelCase__   =matrix_addition(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)

						# construct the new matrix from our 4 quadrants
						lowerCamelCase__   =[]
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(top_left[i] + top_right[i]							)
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(bot_left[i] + bot_right[i]							)
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							)[1] != matrix_dimensions(__lowerCAmelCase							)[0]:
												lowerCamelCase__   =(
												    "Unable to multiply these matrices, please check the dimensions.\n"
												    F'''Matrix A: {matrixa}\n'''
												    F'''Matrix B: {matrixa}'''
												)
												raise Exception(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)

						if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
												return [matrixa, matrixa]

						lowerCamelCase__   =max(*__lowerCAmelCase  ,       *__lowerCAmelCase							)
						lowerCamelCase__   =int(math.pow(2  ,       math.ceil(math.loga(__lowerCAmelCase							)							)							)							)
						lowerCamelCase__   =matrixa
						lowerCamelCase__   =matrixa

						# Adding zeros to the matrices so that the arrays dimensions are the same and also
						# power of 2
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Removing the additional zeros
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								final_matrix[i].pop()
												else:
																		final_matrix.pop()
						return final_matrix


if __name__ == "__main__":
	a				=[
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 2, 3, 1],
	]
	a				=[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
	print(strassen(matrixa, matrixa))


